| 2009 |
|---|
| 58 | EE | Einoshin Suzuki:
Negative Encoding Length as a Subjective Interestingness Measure for Groups of Rules.
PAKDD 2009: 220-231 |
| 57 | EE | Einoshin Suzuki:
Discovering Action Rules That Are Highly Achievable from Massive Data.
PAKDD 2009: 713-722 |
| 2008 |
|---|
| 56 | | Régis Gras,
Einoshin Suzuki,
Fabrice Guillet,
Filippo Spagnolo:
Statistical Implicative Analysis, Theory and Applications
Springer 2008 |
| 55 | | Takashi Washio,
Einoshin Suzuki,
Kai Ming Ting,
Akihiro Inokuchi:
Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, PAKDD 2008, Osaka, Japan, May 20-23, 2008 Proceedings
Springer 2008 |
| 54 | EE | Shin Ando,
Einoshin Suzuki:
Unsupervised Cross-Domain Learning by Interaction Information Co-clustering.
ICDM 2008: 13-22 |
| 53 | EE | Einoshin Suzuki:
Pitfalls for Categorizations of Objective Interestingness Measures for Rule Discovery.
Statistical Implicative Analysis 2008: 383-395 |
| 2007 |
|---|
| 52 | | Vincent Corruble,
Masayuki Takeda,
Einoshin Suzuki:
Discovery Science, 10th International Conference, DS 2007, Sendai, Japan, October 1-4, 2007, Proceedings
Springer 2007 |
| 51 | | Einoshin Suzuki:
Peut-on Capturer la Sémantique à Travers la Syntaxe ? - Découverte des Règles d'Exception Simultanée.
EGC 2007: 1 |
| 50 | | Régis Gras,
Pascale Kuntz,
Einoshin Suzuki:
Une règle d'exception en Analyse Statistique Implicative.
EGC 2007: 87-98 |
| 49 | EE | Marie Agier,
Jean-Marc Petit,
Einoshin Suzuki:
Unifying Framework for Rule Semantics: Application to Gene Expression Data.
Fundam. Inform. 78(4): 543-559 (2007) |
| 48 | EE | Masatoshi Jumi,
Muneaki Ohshima,
Ning Zhong,
Hideto Yokoi,
Katsuhiko Takabayashi,
Einoshin Suzuki:
Spiral Removal of Exceptional Patients for Mining Chronic Hepatitis Data.
New Generation Comput. 25(3): 223-234 (2007) |
| 2006 |
|---|
| 47 | EE | Yukihiro Nakamura,
Shin Ando,
Kenji Aoki,
Hiroyuki Mano,
Einoshin Suzuki:
Strategy Diagram for Identifying Play Strategies in Multi-view Soccer Video Data.
Discovery Science 2006: 173-184 |
| 46 | | Jérôme Maloberti,
Shin Ando,
Einoshin Suzuki:
Classification non-supervisée de données relationnelles.
EGC 2006: 389-390 |
| 45 | EE | Shin Ando,
Einoshin Suzuki:
An Information Theoretic Approach to Detection of Minority Subsets in Database.
ICDM 2006: 11-20 |
| 44 | EE | Nicolas Durand,
Bruno Crémilleux,
Einoshin Suzuki:
Visualizing Transactional Data with Multiple Clusterings for Knowledge Discovery.
ISMIS 2006: 47-57 |
| 43 | EE | Einoshin Suzuki,
Shin Ando,
Masayuki Hirose,
Masatoshi Jumi:
Intuitive Display for Search Engines Toward Fast Detection of Peculiar WWW Pages.
WImBI 2006: 341-352 |
| 42 | EE | Einoshin Suzuki:
Data Mining Methods for Discovering Interesting Exceptions from an Unsupervised Table.
J. UCS 12(6): 627-653 (2006) |
| 2005 |
|---|
| 41 | EE | Shin Ando,
Einoshin Suzuki,
Shigenobu Kobayashi:
Sample based crowding method for multimodal optimization in continuous domain.
Congress on Evolutionary Computation 2005: 1867-1874 |
| 40 | EE | Masanori Yoshinaga,
Yukihiro Nakamura,
Einoshin Suzuki:
Mini-Car-Soccer as a testbed for granular computing.
GrC 2005: 92-97 |
| 39 | EE | Masatoshi Jumi,
Einoshin Suzuki,
Muneaki Ohshima,
Ning Zhong,
Hideto Yokoi,
Katsuhiko Takabayashi:
Multi-strategy Instance Selection in Mining Chronic Hepatitis Data.
ISMIS 2005: 475-484 |
| 38 | EE | Marie Agier,
Jean-Marc Petit,
Einoshin Suzuki:
Towards Ad-Hoc Rule Semantics for Gene Expression Data.
ISMIS 2005: 494-503 |
| 37 | EE | Einoshin Suzuki:
Worst Case and a Distribution-Based Case Analyses of Sampling for Rule Discovery Based on Generality and Accuracy.
Appl. Intell. 22(1): 29-36 (2005) |
| 36 | EE | Einoshin Suzuki,
Jan M. Zytkow:
Unified algorithm for undirected discovery of exception rules.
Int. J. Intell. Syst. 20(7): 673-691 (2005) |
| 2004 |
|---|
| 35 | | Einoshin Suzuki,
Setsuo Arikawa:
Discovery Science, 7th International Conference, DS 2004, Padova, Italy, October 2-5, 2004, Proceedings
Springer 2004 |
| 34 | EE | Masayuki Hirose,
Einoshin Suzuki:
Using WWW-Distribution of Words in Detecting Peculiar Web Pages.
Discovery Science 2004: 355-362 |
| 33 | EE | Jérôme Maloberti,
Einoshin Suzuki:
An Efficient Algorithm for Reducing Clauses Based on Constraint Satisfaction Techniques.
ILP 2004: 234-251 |
| 32 | EE | Einoshin Suzuki:
Undirected Exception Rule Discovery as Local Pattern Detection.
Local Pattern Detection 2004: 207-216 |
| 2003 |
|---|
| 31 | | Ning Zhong,
Zbigniew W. Ras,
Shusaku Tsumoto,
Einoshin Suzuki:
Foundations of Intelligent Systems, 14th International Symposium, ISMIS 2003, Maebashi City, Japan, October 28-31, 2003, Proceedings
Springer 2003 |
| 30 | EE | Yuu Yamada,
Einoshin Suzuki,
Hideto Yokoi,
Katsuhiko Takabayashi:
Experimental Evaluation of Time-Series Decision Tree.
Active Mining 2003: 190-209 |
| 29 | EE | Jérôme Maloberti,
Einoshin Suzuki:
Improving Efficiency of Frequent Query Discovery by Eliminating Non-relevant Candidates.
Discovery Science 2003: 220-232 |
| 28 | EE | Einoshin Suzuki,
Takeshi Watanabe,
Hideto Yokoi,
Katsuhiko Takabayashi:
Detecting Interesting Exceptions from Medical Test Data with Visual Summarization.
ICDM 2003: 315-322 |
| 27 | | Yuu Yamada,
Einoshin Suzuki,
Hideto Yokoi,
Katsuhiko Takabayashi:
Decision-tree Induction from Time-series Data Based on a Standard-example Split Test.
ICML 2003: 840-847 |
| 26 | EE | Masaki Narahashi,
Einoshin Suzuki:
Detecting Hostile Accesses through Incremental Subspace Clustering.
Web Intelligence 2003: 337-343 |
| 2002 |
|---|
| 25 | EE | Masaki Narahashi,
Einoshin Suzuki:
Subspace Clustering Based on Compressibility.
Discovery Science 2002: 435-440 |
| 24 | | Fumio Takechi,
Einoshin Suzuki:
Finding an Optimal Gain-Ratio Subset-Split Test for a Set-Valued Attribute in Decision Tree Induction.
ICML 2002: 618-625 |
| 23 | EE | Shutaro Inatani,
Einoshin Suzuki:
Data Squashing for Speeding Up Boosting-Based Outlier Detection.
ISMIS 2002: 601-612 |
| 22 | EE | Yuta Choki,
Einoshin Suzuki:
Iterative Data Squashing for Boosting Based on a Distribution-Sensitive Distance.
PKDD 2002: 86-98 |
| 21 | EE | Einoshin Suzuki:
In Pursuit of Interesting Patterns with Undirected Discovery of Exception Rules.
Progress in Discovery Science 2002: 504-517 |
| 20 | EE | Einoshin Suzuki:
Undirected Discovery of Interesting Exception Rules.
IJPRAI 16(8): 1065-1086 (2002) |
| 2001 |
|---|
| 19 | EE | Einoshin Suzuki:
Worst-Case Analysis of Rule Discovery.
Discovery Science 2001: 365-377 |
| 18 | EE | Einoshin Suzuki,
Masafumi Gotoh,
Yuta Choki:
Bloomy Decision Tree for Multi-objective Classification.
PKDD 2001: 436-447 |
| 2000 |
|---|
| 17 | EE | Einoshin Suzuki:
Issues in Organizing a Successful Knowledge Discovery Contest.
Discovery Science 2000: 282-284 |
| 16 | | Einoshin Suzuki,
Shusaku Tsumoto:
Evaluating Hypothesis-Driven Exception-Rule Discovery with Medical Data Sets.
PAKDD 2000: 208-211 |
| 15 | | Farhad Hussain,
Huan Liu,
Einoshin Suzuki,
Hongjun Lu:
Exception Rule Mining with a Relative Interestingness Measure.
PAKDD 2000: 86-97 |
| 14 | EE | Einoshin Suzuki,
Jan M. Zytkow:
Unified Algorithm for Undirected Discovery of Execption Rules.
PKDD 2000: 169-180 |
| 13 | EE | David Ramamonjisoa,
Einoshin Suzuki,
Issam A. Hamid:
Research Topics Discovery from WWW by Keywords Association Rules.
Rough Sets and Current Trends in Computing 2000: 412-419 |
| 1999 |
|---|
| 12 | EE | Einoshin Suzuki:
Scheduled Discovery of Exception Rules.
Discovery Science 1999: 184-195 |
| 11 | EE | Shinsuke Sugaya,
Einoshin Suzuki:
Normal Form Transformation for Object Recognition Based on Support Vector Machines.
Discovery Science 1999: 306-315 |
| 10 | EE | Einoshin Suzuki,
Toru Ohno:
Prediction Rule Discovery Based on Dynamic Bias Selection.
PAKDD 1999: 504-508 |
| 9 | | Shinsuke Sugaya,
Einoshin Suzuki,
Shusaku Tsumoto:
Support Vector Machines for Knowledge Discovery.
PKDD 1999: 561-567 |
| 8 | | Einoshin Suzuki,
Hiroki Ishihara:
Visualizing Discovered Rule Sets with Visual Graphs Based on Compressed Entropy Density.
RSFDGrC 1999: 414-422 |
| 1998 |
|---|
| 7 | | Einoshin Suzuki:
Simultaneous Reliability Evaluation of Generality and Accuracy for Rule Discovery in Databases.
KDD 1998: 339-343 |
| 6 | | Einoshin Suzuki,
Yves Kodratoff:
Discovery of Surprising Exception Rules Based on Intensity of Implication.
PKDD 1998: 10-18 |
| 1997 |
|---|
| 5 | | Einoshin Suzuki:
Autonomous Discovery of Reliable Exception Rules.
KDD 1997: 259-262 |
| 1996 |
|---|
| 4 | | Einoshin Suzuki,
Masamichi Shimura:
Exceptional Knowledge Discovery in Databases Based on Information Theory.
KDD 1996: 275-278 |
| 1994 |
|---|
| 3 | EE | Pierre Morizet-Mahoudeaux,
Einoshin Suzuki,
Setsuo Ohsuga:
Knowledge-Based Handling of Design Expertise.
ICDE 1994: 368-374 |
| 1993 |
|---|
| 2 | EE | Einoshin Suzuki,
Tatsuya Akutsu,
Setsuo Ohsuga:
Knowledge-based system for computer-aided drug design.
Knowl.-Based Syst. 6(2): 114-126 (1993) |
| 1991 |
|---|
| 1 | EE | Tatsuya Akutsu,
Einoshin Suzuki,
Setsuo Ohsuga:
Logic-based approach to expert systems in chemistry.
Knowl.-Based Syst. 4(2): 103-116 (1991) |
